A TUNING STRATEGY FOR FACE RECOGNITION IN ROBOTIC APPLICATION

Thierry Germa, Romain Rioux, Michel Devy, Frédéric Lerasle

2009

Abstract

This paper deals with video-based face recognition and tracking from a camera mounted on a mobile robot companion. All persons must be logically identified before being authorized to interact with the robot while continuous tracking is compulsory in order to estimate the position of this person. A first contribution relates to experiments of still-image-based face recognition methods in order to check which image projection and classifier associations lead to the highest performance of the face database acquired from our robot. Our approach, based on Principal Component Analysis (PCA) and Support Vector Machines (SVM) improved by genetic algorithm optimization of the free-parameters, is found to outperform conventional appearance-based holistic classifiers (eigenface and Fisherface) which are used as benchmarks. The integration of face recognition, dedicated to the previously identified person, as intermittent features in the particle filtering framework is well-suited to this context as it facilitates the fusion of different measurement sources by positioning the particles according to face classification probabilities in the importance function. Evaluations on key-sequences acquired by the mobile robot in crowded and continuously changing indoor environments demonstrate the tracker robustness against such natural settings. The paper closes with a discussion of possible extensions.

References

  1. Adini, Y., Moses, Y., and Ullman, S. (1997). Face recognition: the problem of compensating for changes in illumination direction. 19(7):721-732.
  2. Arulampalam, S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for on-line nonlinear/non-gaussian bayesian tracking. Trans. on Signal Processing, 2(50):174-188.
  3. Belhumeur, P., Hespanha, J., and Kriegman, D. (1996). Eigenfaces vs. fisherfaces. In European Conf. on Computer Vision (ECCV'96), pages 45-58.
  4. Gavrila, D. and Munder, S. (2007). Multi-cue pedestrian detection and tracking from a moving vehicle. Int. Journal of Computer Vision (IJCV'07), 73(1):41-59.
  5. Germa, T., Brèthes, L., Lerasle, F., and Simon, T. (2007). Data fusion and eigenface based tracking dedicated to a tour-guide robot. In Int. Conf. on Vision Systems (ICVS'07), Bielefeld, Germany.
  6. Heseltine, T., Pears, N., and Austin, J. (2002). Evaluation of image pre-processing techniques for eigenface based recognition. In SPIE: Image and Graphics, pages 677-685.
  7. Isard, M. and Blake, A. (1998). I-CONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. In European Conf. on Computer Vision (ECCV'98), pages 893-908.
  8. Jonsson, K., Matas, J., Kittler, J., and Li, Y. (2000). Learning support vectors for face verification and recognition. In Int. Conf. on Face and Gesture Recognition (FGR'00), pages 208-213, Grenoble, France.
  9. Lam, K. and Yan, H. (98). An analytic-to-holistic approach fo face recognition based on a single frontal view. 7(20):673-686.
  10. Lee, K., Ho, J., Yang, M., and Kriegman, D. (2003). Videobased face recognition using probabilistic appearance manifolds. Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, 1:I-313-I-320 vol.1.
  11. Pérez, P., Vermaak, J., and Blake, A. (2004). Data fusion for visual tracking with particles. Proc. IEEE, 92(3):495- 513.
  12. Provost, F. and Fawcett, T. (2001). Robust classification for imprecise environments. Machine Learning, 42(3):203-231.
  13. Seo, K. (2007). A GA-based feature subset selection and parameter optimization of SVM for content-based image retrieval. In Int. Conf. on Advanced Data Mining and Applications (ADMA'07), pages 594-604, Harbin, China.
  14. Shan, S., Gao, W., and Zhao, D. (2003). Face recognition based on face-specific subspace. Int. Journal of Imaging Systems and Technology, 13(1):23-32.
  15. Turk, M. and Pentland, A. (1991). Face recognition using eigenfaces. In Int. Conf. on Computer Vision and Pattern Recognition (CVPR'91), pages 586-591.
  16. Viola, P. and Jones, M. (2003). Fast multi-view face detection. In Int. Conf. on Computer Vision and Pattern Recognition (CVPR'03).
  17. Wu, T., Lin, C., and Weng, R. (2004). Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5:975- 1005.
  18. Xu, L. and Li, C. (2006). Multi-objective parameters selection for SVM classification using NSGA-II. In Industrial Conference on Data Mining (ICDM'06), pages 365-376.
  19. Zhou, S., Chellappa, R., and Moghaddam, B. (2004). Visual tracking and recognition using appearance-adaptive models in particle filters. Trans. on Image Processing, 13(11):1491-1506.
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Paper Citation


in Harvard Style

Germa T., Rioux R., Devy M. and Lerasle F. (2009). A TUNING STRATEGY FOR FACE RECOGNITION IN ROBOTIC APPLICATION . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 514-522. DOI: 10.5220/0001800105140522


in Bibtex Style

@conference{visapp09,
author={Thierry Germa and Romain Rioux and Michel Devy and Frédéric Lerasle},
title={A TUNING STRATEGY FOR FACE RECOGNITION IN ROBOTIC APPLICATION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={514-522},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001800105140522},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - A TUNING STRATEGY FOR FACE RECOGNITION IN ROBOTIC APPLICATION
SN - 978-989-8111-69-2
AU - Germa T.
AU - Rioux R.
AU - Devy M.
AU - Lerasle F.
PY - 2009
SP - 514
EP - 522
DO - 10.5220/0001800105140522